Researchers investigated 3D point clouds models to understand and analyze them using handy-crafted features [1]}, [2]}. These features are extracted and categorized into two parts: local features and global features. Researchers feed these features into traditional machine learning algorithms to obtain results. However, these features are biased and may not work for all 3D model shapes. To address this challenge, scientists use deep learning with PointNet [3]} in 2017 and further PointNet\(++\)  [4]}. So, deep learning has been widely used in 3D model classification with the base of PointNet such as DensePoint [5]}. We discuss the 3D dataset and the difference between deep learning algorithms such as PointNet and DGCNN.
